Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/hoavdc/codexkit/codexkit-cx-qbr-preparernpx skills add hoavdc/CodexKit --skill codexkit-cx-qbr-preparergit clone --depth 1 https://github.com/hoavdc/CodexKitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hoavdc/codexkit/codexkit-cx-qbr-preparer)<a href="https://agentmods.dev/skills/hoavdc/codexkit/codexkit-cx-qbr-preparer"><img src="https://agentmods.dev/badge/skills/hoavdc/codexkit/codexkit-cx-qbr-preparer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00075 | $0.00670 |
| Opus 5 | $0.00037 | $0.00335 |
| Sonnet 5 | $0.00015 | $0.00134 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
codexkit-cx-qbr-preparer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CX QBR Preparer
Purpose
Build a review pack that turns account data into a value and renewal conversation.
When to use
- A customer QBR or executive account review is coming up.
- A team needs a structured success-plan update.
- An account shows renewal or adoption risk and needs a focused conversation.
When not to use
- The task is only to polish slide visuals.
- The request is for prospecting or pre-sales outreach.
Inputs
- customer goals, stakeholders, and prior commitments
- adoption, usage, support, and ROI signals
- open risks, blockers, roadmap relevance, and renewal context
- audience and meeting objective
Procedure
- Start from the customer's goals, not your product features.
- Summarize what was committed last period and what actually happened.
- Quantify adoption health and value where evidence exists.
- Surface risks, open issues, and trust-sensitive misses honestly.
- Build the next-quarter success plan with mutual commitments.
- End with the decisions or asks needed from both sides.
Output
- QBR agenda and story arc
- results against commitments
- adoption and ROI narrative
- risk and open-issue summary
- next-quarter plan with owners and dates
Definition of done
- The review is anchored in customer outcomes.
- Risks and misses are visible, not buried.
- The next-quarter plan has mutual commitments and follow-up actions.
Examples
- "Prepare a QBR for this enterprise customer using usage, support, and renewal notes."
- "Turn our account health data into an executive review pack with risks and next-quarter commitments."
Quality Criteria
- Data sources and assumptions are explicitly stated
- Calculations are reproducible from provided inputs
- Visualizations or tables have clear labels, units, and time ranges
- Caveats and confidence levels are documented for estimates
Verification (4C)
| Check | Question |
|---|---|
| Correctness | Are formulas, aggregations, and statistical methods applied correctly? |
| Completeness | Does the analysis cover all requested metrics and time ranges? |
| Context-fit | Are the chosen metrics relevant to the business question being answered? |
| Consequence | If this data were used for a decision today, what blind spots remain? |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 85 lines · 75 tokens per session scan A a246c3455ca9
codexkit-cx-qbr-preparer is a skill published in the GitHub repository hoavdc/CodexKit (21 stars, last pushed 3mo ago), licensed MIT. It adds 75 tokens to every session and 670 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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